Study on Recognitions of Luxury Brands by Using Social Big Data

소셜 빅데이터를 활용한 럭셔리 브랜드 인식 연구

Kim, Sung Soo;Kim, Young Sam

  • Received : 2016.01.05
  • Accepted : 2016.02.20
  • Published : 2016.02.28


This study analyzes consumers' preference trend, positive and negative factors in regards to luxury brands by researching changes in the consumer awareness of luxury brands, preference trends and psychological awareness based on big data to suggest a creative business strategy for corporations that can help Korean brands enter global luxury brand markets. The study results are as follows. Preferred items (consumer) psychology, positive awareness and negative awareness were derived based on the last five years of social big data on Korean consumers' preferred brands. First, the Korean consumers' preferred brands for the recent five years indicated that Dolce & Gabbana (2013), ESCADA (2012), Gucci (2011, 2009) and Chanel (2010) were most preferred and Prada, Louis Vuitton, Hermes, Burberry, Fendi, Givenchy and Dior were also shown to be preferred brands. Second, bags (such as shoulder bags) were shown to be the most preferred items for luxury brand items that consumers wished to own. Third, it was analyzed that keywords for consumer psychology in regards to luxury brands included: diverse, new, outstanding, overwhelming, luxurious, glamorous, worldwide, famous, success and good. Fourth, consumers' positive awareness regarding luxury brands included: diverse, luxury, famous, outstanding, perfect, bright and luxurious. Fifth, negative awareness included: price factors of expensive, high price and excessive as well as factors to be improved upon such as old, bland, flashy, crude, unfriendly and fake.


social big data;luxury brand;consumers' sentiment;consumers' preferred brands


  1. Han, S. H. (2014). A study on the evaluation of data collection tools for big data utilization. Unpublished master's thesis, Dankook University, Yongin.
  2. International Data Corporation. (2011). Extracting value from chaos. Framingham: International Data Corporation.
  3. Jang, J. H. (2015). Suggestion for jewelry marketing utilizing big data. Korea Science & Art Forum, 19(-), 539-599. doi:10.17548/ksaf.2015.03.19.593
  4. Cho, S. H. (2014). SSD-based hybrid storage system for efficient storage of big data. Unpublished doctoral dissertation, Mokpo National University, Mokpo.
  5. Gartner. (2011). Big data analytics. Stamford: Gartner.
  6. Kim, S. S., & Kim, Y. S. (2014). Study of luxury brand awareness through an analysis of social big data. Proceedings of the Proceedings of the Society of Fashion & Textile Industry, Fall Conference, Korea, pp. 35-36.
  7. Kim, Y. M., Hwang, M. Y., Kim, T. H., Jeong, C. H., & Jeong, D. H. (2015). Big data mining for natural disaster analysis. Journal of the Korean Data & Information Science Society, 26(5), 1105-1115. doi:10.7465/jkdi.2015.26.5.1105
  8. Lee, C. W. (2014a). A study of using big data for global online business -Focusing on e-trade-. Unpublished master's thesis, Hansung University, Seoul.
  9. Lee, D. H., Kang, H. G., Kim, S. H., & Lee, C. M. (2013). Autocorrelation analysis of the sentiment with stock information appearing on big-data. The Korean Journal of Financial Engineering, 12(2), 79-96.
  10. Lee, H. K. (2014b). The classification of source data types through the analysis of big data application cases : Focused on BI&A applications and U-City service categories. Unpublished master's thesis, Dankook University, Yongin.
  11. Lee, H. S. (2014c). A study on drought area and severity by big data analysis. Unpublished master's thesis, Hanseo University, Seosan.
  12. McKinsey Global Institute. (2011). Big Data: The next frontier for innovation, competition, and productivity. New York: Author.
  13. National Information Society Agency. (2011). New engine for value creation, new possibility of big data and coping strategy. Seoul: NIA.
  14. National Information Society Agency. (2013). Era of big data for a new future. Seoul: NIA.
  15. Park, J. Y. (2014). A study on the establishing of efficient operational strategy by using the big data-Casino industry-. Journal of Hotel & Resort, 13(1), 5-22.
  16. Samsung Economic Research Institute. (2012). Big Data: Epicenter of Industrial Diastrophism. Seoul: SERI.
  17. Song, T. M., Song, J. Y., & Cheon, M. K. (2015). Predicting tobacco risk factors by using social big data. Journal of the Korean Data & Information Science Society, 26(5), 1047-1059. doi:10.7465/jkdi.2015.26.5.1047